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首页> 外文期刊>Publications of the Astronomical Society of the Pacific >Quantitative Method for the Optimal Subtraction of Continuum Emission from Narrow-band Images: Skewness Transition Analysis
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Quantitative Method for the Optimal Subtraction of Continuum Emission from Narrow-band Images: Skewness Transition Analysis

机译:从窄带图像中最佳减去连续谱的定量方法:偏度转换分析

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摘要

We present an objective method to remove the stellar continuum emission from narrow–band images to derive emission–line images. The method is based on the skewness of the pixel histogram of the residual images. Specifically, we exploit a transition in the skewness of the signal in the continuum–subtracted image, which appears when the image changes from being undersubtracted to oversubtracted. Tests on one–dimensional artificial images demonstrate that the transition identifies the optimal scaling factor μ to be used on the broad-band image IB in order to produce the optimal line–emission image I_E, i.e., I_E = I_(N-μ)I_B, with I_N the original (unsubtracted) narrow–band image. The advantage of this method is that it uses all information–bearing pixels in the final image, and not just a subset of those pixels (the latter being common in many traditional approaches to stellar continuum removal from narrow–band images). We apply our method to actual images, both from ground–based and space facilities, in particular to WFPC2 and ACS images from the Hubble Space Telescope, and we show that it is successful irrespective of the nature of the sources (point-like or extended). We also discuss the impact on the accuracy of the method of nonoptimal images, such as those containing saturated sources or nonuniform background, and present workarounds for those problems.
机译:我们提出了一种客观方法,可从窄带图像中去除恒星连续体发射,以得出发射线图像。该方法基于残差图像的像素直方图的偏度。具体来说,我们利用连续谱减去后的图像中信号的偏度的过渡,当图像从扣除不足变为过度扣除时出现。对一维人造图像的测试表明,该过渡确定了要在宽带图像IB上使用的最佳缩放因子μ,以便生成最佳的线发射图像I_E,即I_E = I_(N-μ)I_B ,使用I_N原始(未减去)窄带图像。这种方法的优点是它使用了最终图像中所有带有信息的像素,而不仅仅是这些像素的子集(后者在从窄带图像中去除恒星连续体的许多传统方法中很常见)。我们将我们的方法应用于来自地面和太空设施的实际图像,特别是应用于哈勃太空望远镜的WFPC2和ACS图像,并且我们证明了该方法是成功的,无论其来源是什么(点状或扩展状) )。我们还将讨论对非最佳图像方法(例如包含饱和光源或背景不均匀的图像)方法准确性的影响,并针对这些问题提出解决方法。

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